Gongde Guo1, Kai Yu1, Hui Wang2, Song Lin1, *, Yongzhen Xu1, Xiaofeng Chen3
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1397-1409, 2020, DOI:10.32604/cmc.2020.011399
Abstract As an important branch of machine learning, clustering analysis is widely used
in some fields, e.g., image pattern recognition, social network analysis, information
security, and so on. In this paper, we consider the designing of clustering algorithm in
quantum scenario, and propose a quantum hierarchical agglomerative clustering
algorithm, which is based on one dimension discrete quantum walk with single-point
phase defects. In the proposed algorithm, two nonclassical characters of this kind of
quantum walk, localization and ballistic effects, are exploited. At first, each data point is
viewed as a particle and performed this kind of quantum walk with a parameter,… More >
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